In order to improve the effect of image generation and reduce the loss of high-frequency information,a new generative adversarial network model based on deep learning is proposed to realize super-resolution reconstruction of single image.The network structure,residual network and convolution parameters are modified based on SRGAN method.The DIV2K data set is used to train the network model.The quality of the generated images is tested and evaluated by two evaluation criteria,the peak signal-to-noise ratio(PSNR)and the structural similarity index(SSIM).Experimental results show that,compared with the SRGAN method,the images produced by the new method has better visual effect,clearer texture and better objective and subjective evaluation.